Results 131 to 140 of about 2,103,105 (303)

Integrating Genetics With Epidemiological Measurements Identifies Burden QTLs of Inflammatory Bowel Disease across 20 Countries

Advanced Science, EarlyView.
The burden quantitative trait locus (bQTL) analysis integrates epidemiological data from GBD study with genotype of 2 621 978 SNPs from 2414 individuals in 1000 Genomes Project across 20 countries. rs7633471 shows the strongest effect that each increase in centi‐allele frequency of the allele C associated with 10.00 decrease of age‐standardized ...
Chen Sun, Siyu Wei, Junxian Tao, Haiyan Chen, Chi Yan, Jiacheng Wang, Jing Xu, Lian Duan, Yuanbo Zhan, Yuping Zou, Ruilin Li, Linna Yuan, Wei She, Songlin Lu, Ning Wang, Qinduo Ren, Yan Guo, Yuan Xu, Chang Wang, Hongsheng Tian, Chen Zhang, Yu Dong, Guoping Tang, Zhenwei Shang, Yongshuai Jiang, Wenhua Lv, Mingming Zhang, Hongchao Lyu   +27 more
wiley   +1 more source

Bayesian Neural Network-Assisted Parameter Estimation of the Transmuted Teissier Distribution for Environmental Data Modeling

IEEE Access
Machine learning has evolved into a potent tool for analysing patterns and making predictions from complex data. In this machine learning era, we employed neural network techniques to estimate the parameters of statistical distributions.
P. T. Amrutha, C. S. Rajitha
doaj   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Bayesian Inference for PCFGs via Markov Chain Monte Carlo [PDF]

, 2007
This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximum-likelihood estimation using Inside-Outside algorithm.
Johnson, Mark, Griffiths, Thomas, Goldwater, Sharon; id_orcid   +2 more
core  

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

Performance Quantile Regression and Bayesian Quantile Regression in Dealing with Non-normal Errors (Case Study on Simulated Data)

Numerical: Jurnal Matematika dan Pendidikan Matematika
This research discusses the performance of quantile regression and Bayesian quantile regression methods. Quantile regression uses parameter estimation by maximizing the value of the likelihood function, while Bayesian quantile regression uses parameter ...
Lilis Harianti Hasibuan, Ferra Yanuar, Vika Pradinda Harahap, Latifatul Qalbi   +3 more
doaj   +1 more source

An approach to non-linear Bayesian forecasting problems with applications [PDF]

This thesis is devoted to the analysis and modelling of time series and it is concentrated on models and techniques which are of practical value. In particular we developed a wide class of non-linear dynamic models which are useful in the handling of ...
Migon, Helio dos Santos
core  

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Automating Chemical Reasoning in High‐Throughput Phase Identification With a Probabilistic, LLM‐Guided Framework

Advanced Science, EarlyView.
Autonomous laboratories can now synthesize materials faster than experts can interpret the resulting diffraction data. A probabilistic framework combines refinement‐fit metrics with large language model‐derived chemical reasoning to rank competing phase interpretations and flag those unsuitable for autonomous use.
Olympia Dartsi, Lauren N. Walters, Amalie E. Trewartha, Steven B. Torrisi, Amanda Volk, Liam Joyce, Gerbrand Ceder, Anubhav Jain   +7 more
wiley   +1 more source